Selection from the list of topics

What do the numbers stand for?
What is the purpose of the standard error?
When are two results statistically different?
When is it not sufficient to consider only the standard error?
Two types of changes?
Change analyses: When are the results (not) additive?
Why is a dot (“.”) sometimes given in the cells for standard error or estimated value?
Why are no standard error values shown in some tables?
Negative values for growing stock, increment or fellings?

1. What do the numbers stand for?

The tables mainly show results that were calculated using statistical methods from the data of the sample inventory of the National Forest Inventory (NFI). Such results always consist of two figures: (1) the estimated value and (2) the sampling error, the so-called standard error.

Example 1: Estimated value and standard error

NFI5

volume of deadwood (stemwood)
ownership (2 categories)

regional demarcation: production region

unit: m³/ha

evaluation area: accessible forest without shrub forest

grid: 1.4 km grid, subgrids 1-5

state 2018/26
	production region
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
ownership (2 categories)	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
public	30	10	17	13	37	9	36	6	26	10	30	4
private	27	17	20	12	50	10	32	8	39	13	34	5
total	29estimated value	9standard error	19	9	44	7	35	5	29	8	32	3

The tables usually show the relative (percentage) standard error (“±%”), but occasionally – in the case of estimated percentages – the absolute standard error (“±”).

top

2. What is the purpose of the standard error?

The standard error can be used to define confidence intervals around the estimated value, which contain the true value of the population with a certain statistical certainty.

The statistical certainty is:

68% if the confidence interval is calculated using the standard error
(68% confidence interval = estimated value ± standard error), and
95% if the confidence interval is calculated using twice the standard error
(95% confidence interval = estimated value ± 2 × standard error*)

Example 2: Calculating confidence intervals

LFI5
volume of deadwood (stemwood)
ownership (2 categories)
regional demarcation: production region
unit: m³/ha
evaluation area: accessible forest without shrub forest
grid: 1.4 km grid, subgrids 1-5
state 2018/26
	production region
	Jura
ownership (2 classes)	m³/ha	±%
public	30	10
private	27	17
total	29	9

Question
What are the 68% and 95% confidence intervals of the above estimates?

Procedure

Calculate the absolute standard error (= relative standard error × estimated value / 100)
Calculate the confidence intervals
- 68% confidence interval = estimated value ± absolute standard error
- 95% confidence interval = estimated value ± 2 × absolute standard error

Answer

			Step 1	Step 2

ownership (2 categories)	volume of deadwood Jura NFI5
	estimated value	standard error		confidence interval
		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha
public	30	10	3	27-33	24-36
private	27	17	5	22-32	17-37
total	29	9	3	26-32calculated with the standard error, i.e. 29 ± 3	23-25calculated with twice the standard error, i.e. 29 ± 2 × 3

The volume of deadwood in the Jura is between 26 and 32 m³/ha with a statistical certainty of 68% and between 23 and 35 m³/ha with a statistical certainty of 95%.

The 95% confidence interval is larger than the 68% confidence interval. This results in higher statistical certainty with the 95% confidence interval.

Example 3: Visualisation of the 68% and 95% confidence intervals

Data: see example 2

When interpreting the results, one must define the level of statistical certainty that one would like to apply to the statement.

The 68% confidence interval is generally used in the National Forest Inventory (NFI).

top

3. When are two results statistically different?*

This can be checked by comparing the confidence intervals of two estimated values:

If the 68% confidence intervals of two estimates do not overlap, it can be assumed with some certainty that the two populations differ.
If the 95% confidence intervals of two estimates do not overlap, it can be assumed with high certainty that the two populations differ.

Example 4: Interpretation of two results from the same inventory

NFI5
growing stock (stemwood)
ownership (2 categories)
regional demarcation: production region
unit: m³/ha
evaluation area: accessible forest without shrub forest
grid: 1.4 km grid, subgrids 1-5
state 2018/26
	production region
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
ownership (2 categories)	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
public	331	3	311	3	405	4	300	3	256	5	314	2
private	387	5	432	4	454	4	348	4	285	6	398	2
total	345	2	363	2	431	2	314	2	262	4	343	1

	case 2						case 1

case 1

Question
Is the growing stock per hectare greater in the Alps than in the Southern Alps?

Procedure

Calculate the absolute standard errors
Calculate the confidence intervals depending on the desired level of statistical certainty:
- 68% confidence intervals (some certainty; absolute standard error)
- 95% confidence intervals (high certainty; twice the absolute standard error)
Assess whether the confidence intervals corresponding to the desired level of statistical certainty do not overlap

Answer

ownership (2 categories)	growing stock NFI5
	Alps					Southern Alps
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha	m³/ha	±%	±m³/ha	m³/ha	m³/ha
total	314	2	6	308-320	302-326	262	4	10	252-272	242-282

Neither the 68%-confidence intervals nor the 95%-confidence intervals overlap (pairs of values in ).

In this case, the level of statistical certainty selected has no influence on the result.

Overall, this means that there is high statistical certainty that the growing stock per hectare is greater in the Alps than in the Southern Alps.

Case 2

Question
Is the growing stock per hectare greater in the Plateau than in the Jura?

Procedure

See case 1.

Answer

ownership (2 categories)	growing stock NFI5
	Jura					Plateau
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha	m³/ha	±%	±m³/ha	m³/ha	m³/ha
total	345	2	7	338-352	331-359	363	2	7	356-370	349-377

The 68%-confidence intervals do not overlap (pairs of values in ).
In contrast, the 95%-confidence intervals overlap (pairs of values in ).

In this case, the level of statistical certainty selected has an influence on the interpretation of the results. At the 68% confidence interval level, the conclusion is that the growing stock per hectare is higher in the Plateau than in the Jura. At the 95% confidence interval level, however, this conclusion cannot be drawn.

Considering both confidence interval levels, it can be said that there is some, but not high, statistical certainty that the growing stock per hectare is greater in the Plateau than in the Jura.

The confidence intervals can also be used to determine whether a value estimated with a sample from the National Forest Inventory (NFI) deviates from a target value (e.g. from Swiss forest policy) or whether a change between two inventories is statistically certain.

Example 5: Interpretation of two results from different inventories

ownership (2 categories)	growing stock Plateau*
	NFI4		NFI5
	m³/ha	±%	m³/ha	±%
public	340	3	309	3
private	447	4	432	4
total	386	2	363	2
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Calculate the absolute standard errors
Calculate the confidence intervals depending on the desired level of statistical certainty:
- 68% confidence intervals (some certainty; absolute standard error)
- 95% confidence intervals (high certainty; twice the absolute standard error)
Assess whether the confidence intervals corresponding to the desired level of statistical certainty do not overlap.

Answer

ownership (2 categories)	growing stock Plateau*
	NFI4					NFI5
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±	m³/ha	m³/ha	m³/ha	±%	±	m³/ha	m³/ha
public	340	3	11	329-351	318-362	309	3	11	298-320	287-331
private	447	4	16	431-463	415-479	432	4	17	415-449	398-466
total	386	2	8	378-394	370-402	363	2	9	354-372	345-381
* accessible forest without shrub forest NFI4/NFI5

The 68%-confidence intervals do not overlap in the public forest (pairs of values in ), but they do in the private forest and the total forest (pairs of values in ). At the certainty level of the 68% confidence intervals, it can be concluded that the growing stock per hectare in the public forest of the Plateau has decreased. For the private forest, as well as for the total forest, the decrease is not statistically certain at this level.

The 95%-confidence intervals overlap in all three categories. At the certainty level of the 95% confidence intervals, it is therefore not certain that the growing stock has decreased, even for the public forest.

See also examples 6 and 7, in which changes between two inventories are interpreted using the more sensitive metric of net change.

For many target variables in the NFI, it is possible to determine whether a change between two inventories is statistically certain, not only by comparing the states in the two inventories (see example 5), but also by analysing the net change** between the two inventories.

To do this, one must first define the level of statistical certainty that one would like to apply to the statement (as done above):

A net change has occurred with some certainty if the relative standard error is less than 100% or if the confidence interval calculated with the absolute standard error (68% confidence interval) does not include the value 0.
A net change has occurred with high certainty if twice the relative standard error is less than 100% or if the confidence interval calculated with twice the absolute standard error (95% confidence interval) does not include the value 0.

Example 6: Interpretation of net changes with the relative standard error

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
	m³/ha	±%
public	-34	30
private	-9	147
total	-23	34
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Define the level of statistical certainty (68% or 95% confidence interval)
Determine whether:
- the relative standard error is less than 100% (certainty level of the 68% confidence interval)
- twice the relative standard error is less than 100% (certainty level of the 95% confidence interval)

Answer

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
		single	double
	m³/ha	±%	±%
public	-34	30	60
private	-9	147	294
total	-23	34	68
* accessible forest without shrub forest NFI4/NFI5

The single relative standard error is less than 100% for both the public forest and the total forest (highlighted values). For the private forest, however, it is greater than 100% (highlighted values). At the certainty level of the 68% confidence interval, it can be concluded that the growing stock in the Plateau has decreased in the public forest and in total. For the private forest, on the other hand, the decrease in stock is not statistically certain at this level.

The same conclusion is reached in this example with the certainty level of the 95% confidence interval, as twice the relative standard error is again less than 100% for both the public forest and the total forest.

Overall, this means that there is high statistical certainty that the growing stock per hectare has decreased in the public forest in the Plateau, as well as overall. For private forests, on the other hand, the decrease in stock is not statistically certain (as twice the relative standard error and the relative standard error are both less than 100%).

Example 7: Interpretation of net changes with the absolute standard error

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
	%	±
public	-10	3
private	-2	3
total	-6	2
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Define the level of statistical certainty (68% or 95% confidence interval)
Calculate the confidence interval based on the defined certainty level
- 68% confidence interval with the absolute standard error
- 95% confidence interval with twice the absolute standard error
Determine whether the corresponding confidence interval does not include the value 0

Answer

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error		confidence interval
		single	double	68%	95%
	%	±	±	%	%
public	-10	3	6	-13 to -7	-16 to -4
private	-2	3	6	-5 to +1	-8 to +4
total	-6	2	4	-8 to -4	-10 to -2
* accessible forest without shrub forest NFI4/NFI5

Neither the 68% nor the 95% confidence interval includes the value 0 for the public forest or for the total forest. At both certainty levels, this leads to the conclusion that the growing stock in the Plateau has decreased in the public forest and in total. For the private forest, on the other hand, the decrease in stock is not statistically certain (as the confidence intervals include the value 0).

Net changes can be interpreted using the absolute or relative standard error (see examples 6 and 7). It is advisable to use the absolute standard error if the estimated value in the table is shown as a percentage (%; as in example 7), and the relative standard error if the estimated value in the table is shown in absolute terms (m³, m³/ha, pc, m²; as in example 6). This means that the standard error can be taken directly from the table and does not need to be converted.

To assess whether a change is statistically certain, it is advisable to analyse the net change between the two inventories wherever possible. Net changes are more sensitive than status comparisons, which makes it easier to detect differences statistically. See examples 6 and 7 (net change) and 7 (comparison of two results from different inventories).

top

4. When is it not sufficient to consider only the standard error?

The National Forest Inventory (NFI) is a sample-based large-scale inventory whose sample areas are arranged on a grid with a mesh size of 1.4 km × 1.4 km. The NFI was designed in such a way that the growing stock for Switzerland as a whole can be predicted with a maximum standard error of 1%.

If results are requested for individual regions or for individual classes, the standard error quickly becomes much larger. This is because the number of sample areas and subjects (e.g. sample trees, young forest plants, pieces of deadwood) analysed for the queried combination of variable expressions* is considerably lower than for the total.

Example 8: Standard error and combination of variable expressions

region	growing stock
	total		maple		sycamore		Norway maple
	estimated value	standard error	estimated value	standard error	estimated value	standard error	estimated value	standard error
	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
Switzerland	343	1	12	5	11	5	1	20
production region "Plateau"	363	2	15	10	14	11	0	50
Canton of Aargau	289	7	15	20	13	20	1	63

The standard error increases as the level of detail increases.

Large standard errors are to be expected if:

forest districts are selected as the regional demarcation or results for small cantons (e.g. Appenzell Innerrhoden, Nidwalden) are queried,
a classification variable with many classes is selected (e.g. tree species in 56 classes),
several classification variables are combined with each other (e.g. main tree species and development stage).

Large standard errors are an indication that the estimate may have been based on too small a number of sample areas or subjects and that the result of the estimate therefore may not be reliable.

For the target variable “number of stems of young forest plants with browsing damage” (browsing intensity), an uncertain estimate is not necessarily recognisable by a large standard error. This is because this target variable is a quotient estimator**, which is – due to the survey method – only based on a small number of assessed young forest plants for rarer tree species (e.g. pine, larch, Arolla pine, oak, chestnut). Accordingly, for this target variable one should always check how many young forest plants were assessed for the individual estimates. As a rule of thumb, at least 30 young forest plants per estimate should have been assessed for browsing in order to obtain a reliable estimate.

top

5. Two types of changes?

There are two types of changes in the National Forest Inventory (NFI):

The first type of change involves specific target variables for change components such as increment, fellings or mortality. These target variables are only available for two consecutive measurement cycles, e.g. NFI4 to NFI5. When change components are analysed, the classification variable expression of the second measurement cycle is generally assigned to the first measurement cycle. These analyses therefore do not take into account any change in the expression (e.g. from private to public ownership) from the earlier to the later measurement cycle.
In the second type of change, the difference in target variables, such as stem number, growing stock or forest area, is used to calculate the net change between two measurement cycles. These target variables are usually used to represent states, e.g. that in NFI5, but can also show the net change between any two measurement cycles, e.g. between NFI1 and NFI5. In these net change analyses, the change in expression is taken into account for some of the classification variables, including “tree condition” and “forest, non-forest”. In this way, it can be seen, for example, that the forest area has increased. For the remaining classification variables, the expressions are regarded as static. This means that the most recent state – whether it was assessed in a measurement cycle (e.g. “primary forest function NFI5”) or taken from an external data source (e.g. “protection forest [2022]”) – is assigned to all measurement cycles.

top

6. Change analyses: When are the results (not) additive?

The results are:

additive if they are reported as the total change between two measurement dates. For example, the fellings for the various production regions reported in cubic metres (m³) can be added together to obtain the total fellings for Switzerland.
not additive if they are reported as a change per year (e.g. m³/year). For example, the fellings for the various production regions reported in cubic metres per year (m³/year) cannot be added together to obtain the total fellings for Switzerland.

Example 9: : Additivity/non-additivity of changes

	growing stock: total change between NFI4 and NFI5 (in 1000 m³)*
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
higher/lower altitude zone	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
lower altitude zone	-3759.0	28	-4909.1	36	-1025.2	133	-317.1	195	3464.1	31	-6546.3	42
higher altitude zone	574.3	117	-328.0	62	1093.0	123	7128.4	21	666.7	93	9134.3	24
total	-3184.7	40	-5237.1	34	67.9	2805	6811.3	23	4130.8	30	2588.1	136
	* accessible forest without shrub forest NFI4/NFI5
	additive

	growing stock: annual change between NFI4 and NFI5 (in 1000 m³/year)*
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
higher/lower altitude zone	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%
lower altitude zone	-421.1	28	-560.6	36	-115.7	133	-35.9	195	389.5	31	-740.7	42
higher altitude zone	64.7	117	-38.3	62	122.4	123	810.4	21	74.9	93	1032.7	24
total	-357.3	40	-598.4	34	7.6	2805	773.8	23	464.4	30	292.7	136
	* accessible forest without shrub forest NFI4/NFI5
	not additive

The non-additivity of changes expressed per year arises because the total change is divided by the average number of years between the two measurement dates in the domain* under consideration, and this average number of years varies slightly depending on the domain.

Example 10: Average number of years per domain

domain		mean number of years*
production region	higher/lower altitude zone
Mittelland	-	8.75
Plateau	higher altitude zone	8.56
Plateau	lower altitude zone	8.76
Southern Alps	-	8.90
Southern Alps	higher altitude zone	8.90
Southern Alps	lower altitude zone	8.89
* between the NFI4 and NFI5 measurements in the accessible forest without shrub forest

top

7. Why is a dot (“.”) sometimes given in the cells for standard error or estimated value?

In the calculation of a results table, data is not always available for all combinations of variable expressions*. In most cases, this indicates that the variable estimated with the relevant target variable does not occur or only occurs very rarely. The value 0 is then usually used. However, as this value is not based on any direct measurements, a dot (“.”) is given for the associated standard error. If reference is made to the assumed value of 0 in the calculation, e.g. in the case of percentages or certain change estimates, no value can be used. In this case, a dot (“.”) is given for the estimated value and the standard error.

For example, no Arolla pines have yet been found and measured in the Plateau in the National Forest Inventory (NFI) (growing stock of Arolla pine by production region). It can therefore be assumed that the values are missing because the Arolla pine actually does not occur in the Plateau, and therefore the growing stock in that region must be 0.

top

8. Why are no standard error values shown in some tables?

The data in these tables originates from a complete survey and not from a sample inventory. Accordingly, it is not necessary to specify a standard error because there is no uncertainty due to sampling.

The forest road network survey that the National Forest Inventory (NFI) periodically carries out with the local forestry services is an example of such a complete survey.

top

9. Negative values for growing stock, increment or fellings?

A negative value is possible if the result could only be calculated on the basis of a few trees. The estimate is therefore not reliable. This is also reflected in the standard error, which is usually extremely large for negative stock, increment or felling values.

Negative values can occur because, when the stemwood volume of the tally trees is determined, the volume estimated solely on the basis of the diameter at breast height (DBH) (tariff volume) is adjusted using the volume of the tally trees estimated on the basis of DBH, diameter at 7 m height and tree height. This procedure enables an unbiased and more accurate calculation of the stemwood volume. However, it can lead to negative values for all target variables that are based on the stemwood volume of the tally trees (e.g. growing stock, increment, fellings) if the number of tally trees in the selected combination of variable expressions* is small.

top

NFI4
growing stock (stemwood)
diameter at breast height (10 classes) · conifers and broadleaves
regional demarcation: forest district (2024)
unit: 1000 m³
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 2009/17
		forest district (2024)
		AG01		AG02		AG03		AG04		AI01		AR01		BE01		BE02		BE03		BE04		BSBL01		BSBL02		BSBL03		BSBL04		FR01		FR02		FR03		FR04		GE01		GL01		GR01		GR02		GR03		GR04		GR05		JU01		LU01		LU02		LU03		NE01		NE02		NE03		NE04		NE06		NW01		OW01		OW02		SG01		SG02		SG03		SG04		SG05		SH01		SO01		SO02		SO03		SO04		SZ01		SZ02		SZ03		TG01		TG02		TG03		TI01		TI02		TI03		TI04		TI05		TI06		TI07		TI08		TI09		UR01		UR02		UR03		VD02		VD03		VD04		VD05		VD06		VD07		VD08		VD09		VD11		VD12		VD14		VD15		VD16		VD18		VD20		VD22		VS01		VS02		VS03		ZG01		ZH01		ZH02		ZH03		ZH04		ZH05		ZH06		ZH07		lakes		Switzerland
diameter at breast height (10 classes)	conifers and broadleaves	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
12-20 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	conifers	78	43	129	33	119	53	89	61	85	38	111	35	871	10	511	13	602	16	295	18	8	48	53	56	47	30	0	.	46	38	160	27	227	23	171	37	0	.	141	39	365	17	244	18	560	20	885	14	1003	12	482	18	221	26	307	25	294	17	27	54	114	30	34	34	48	36	201	41	98	37	170	25	242	29	131	29	122	43	191	22	80	26	63	39	91	35	79	34	35	77	33	70	57	33	189	30	145	27	80	31	110	27	120	38	174	55	84	30	190	23	105	25	28	65	4	75	3	74	143	23	53	40	70	43	97	32	38	35	147	26	85	34	70	40	158	31	86	38	29	48	141	31	31	40	42	36	169	24	87	42	83	42	45	31	50	55	25	57	3	100	31	57	790	12	579	16	455	13	102	27	68	54	55	38	87	31	98	46	24	52	56	50	38	53	0	.	15179	3
	broadleaves	227	25	147	27	124	37	184	22	40	77	74	31	568	15	334	17	457	13	425	16	53	42	69	37	139	25	0	.	61	61	77	34	138	32	225	32	45	49	179	21	99	24	133	25	303	24	415	20	115	33	505	14	114	24	83	38	137	20	76	38	75	30	24	37	36	42	45	29	68	38	38	38	128	28	95	30	100	31	151	25	159	30	132	23	203	24	111	26	120	28	143	36	101	26	226	25	66	52	87	41	119	29	127	36	142	35	123	39	217	26	114	31	443	21	337	18	605	17	530	15	420	20	407	20	57	34	75	56	50	37	114	29	129	30	167	28	64	44	37	52	101	30	76	41	50	43	71	31	73	36	76	30	115	25	56	50	39	54	7	71	32	40	193	18	217	20	570	17	21	77	83	35	55	60	161	33	96	27	94	47	38	31	105	38	0	.	14252	3
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0
	total	305	23	276	24	242	32	274	27	124	37	185	26	1439	9	845	11	1059	11	720	13	61	38	122	31	186	20	0	.	106	37	237	22	365	20	395	26	45	49	320	22	465	15	377	15	862	16	1300	12	1117	11	987	13	335	21	390	22	431	15	103	33	188	24	58	30	83	28	246	35	167	28	208	23	370	22	226	23	222	31	342	19	240	23	195	21	294	21	190	24	155	29	176	36	158	21	415	21	210	26	167	28	229	21	247	28	317	34	206	28	407	18	219	21	470	20	341	18	607	17	673	14	473	18	478	18	154	28	113	41	197	23	199	24	199	26	324	22	149	30	66	42	242	24	106	32	91	34	240	22	160	30	159	28	160	22	105	39	64	45	10	58	63	39	983	10	795	13	1025	12	123	28	151	31	110	40	248	28	194	28	118	41	94	34	143	32	0	.	29431	2
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	conifers	140	29	297	36	210	32	101	52	249	28	308	28	1920	10	1095	14	1070	16	703	20	20	54	56	47	111	31	1	.	116	45	533	27	556	20	332	36	12	71	475	29	871	17	748	17	1057	15	1673	11	1842	10	689	16	333	23	396	26	545	19	116	60	245	24	79	36	85	33	322	27	241	32	450	27	418	25	260	32	279	32	378	24	104	45	310	23	366	31	163	35	123	35	41	43	143	29	182	25	360	24	190	35	224	31	203	45	372	35	104	44	381	22	242	25	53	46	5	100	9	100	309	21	145	37	124	46	223	30	160	37	259	27	198	26	132	38	386	33	165	49	63	54	236	29	58	42	80	36	349	23	180	36	174	41	140	39	115	56	105	48	44	57	78	55	1609	12	1380	14	1431	14	246	27	180	56	94	38	273	37	248	30	120	56	67	46	176	35	0	.	32453	2
	broadleaves	380	20	216	25	220	24	458	29	83	48	272	29	757	16	735	16	947	14	1039	15	94	36	207	29	338	23	0	.	131	30	166	33	413	31	384	31	81	44	380	23	174	29	148	43	195	31	563	19	198	49	927	13	229	27	240	26	339	24	48	61	99	30	88	40	51	38	98	34	217	34	103	61	410	28	260	27	173	38	261	24	372	33	377	24	444	22	129	36	258	28	150	34	367	25	293	28	158	38	164	32	300	32	141	35	198	27	79	34	233	26	119	34	473	21	629	22	866	16	690	18	688	21	646	18	256	34	36	45	48	61	206	33	155	34	235	28	192	33	31	54	380	36	84	44	140	44	127	44	208	37	178	26	189	30	119	38	60	47	15	58	82	45	201	25	265	25	715	18	72	32	94	41	141	33	208	28	194	28	201	31	155	33	162	36	0	.	25048	3
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0
	total	519	18	514	25	430	21	559	26	331	27	580	21	2678	9	1830	11	2017	11	1742	13	115	33	263	26	450	21	1	.	248	30	699	23	969	18	715	24	93	42	854	20	1046	16	897	16	1252	13	2236	10	2039	10	1616	11	562	19	636	20	884	16	164	46	344	21	167	32	136	29	420	23	458	25	553	26	828	20	521	22	452	26	640	20	476	30	687	20	810	19	292	27	381	23	191	30	510	23	474	22	518	21	354	28	524	24	344	31	570	25	183	30	615	18	361	20	526	20	634	21	876	16	999	15	833	19	770	17	479	29	197	33	307	25	404	24	287	28	621	26	357	30	95	49	616	28	142	34	220	35	477	23	387	27	352	27	329	26	233	35	165	42	59	48	160	39	1810	11	1644	12	2146	11	318	24	274	40	235	26	481	26	443	22	321	30	222	28	338	27	0	.	57502	2
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	conifers	234	28	195	32	449	32	276	26	569	25	709	22	3395	9	2067	11	1448	15	1308	16	85	69	52	58	138	32	13	.	229	43	649	25	907	24	497	25	34	100	612	28	1514	15	1052	16	1214	15	2917	10	2869	10	1460	15	510	24	754	21	963	20	113	43	393	31	249	40	134	41	423	26	307	33	466	23	492	21	531	27	286	33	768	21	272	33	572	21	403	22	223	34	190	34	119	44	223	28	550	25	558	23	296	33	436	29	218	31	263	36	255	27	464	25	439	27	46	53	21	62	59	100	350	20	91	38	250	37	247	32	293	34	421	30	211	29	275	37	506	26	143	41	50	75	350	27	153	44	137	32	639	20	149	49	238	28	284	30	62	59	117	42	104	57	124	44	2135	11	1726	13	1946	12	181	37	252	34	241	37	309	25	519	27	229	36	235	49	220	38	0	.	50076	2
	broadleaves	627	22	249	30	252	29	538	29	123	33	301	26	941	18	849	16	1042	14	1120	15	104	46	342	27	577	26	0	.	197	36	325	31	431	27	536	26	106	42	368	24	145	32	232	28	84	37	345	20	98	42	1794	13	396	25	324	40	332	29	102	35	167	50	129	45	127	34	195	27	345	32	39	46	369	30	267	32	375	30	352	25	391	25	558	23	660	19	314	29	490	23	282	30	655	21	254	27	42	48	223	41	371	32	186	29	306	27	56	51	166	37	112	45	441	26	609	20	863	15	450	21	333	22	521	24	141	40	9	72	46	61	239	34	123	39	209	31	218	36	63	48	416	29	89	36	146	48	81	36	208	28	401	27	354	30	188	32	51	50	47	54	137	38	129	35	78	38	375	20	155	33	194	34	123	30	318	25	313	25	223	33	158	38	73	39	0	.	28835	3
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	861	18	444	23	701	24	814	22	692	24	1011	20	4336	8	2916	10	2490	11	2428	12	189	42	395	26	715	23	13	.	426	29	974	20	1338	19	1033	19	141	45	980	21	1660	14	1285	15	1298	14	3263	9	2966	10	3253	10	906	19	1078	21	1295	17	214	31	560	26	378	32	261	30	618	21	652	24	505	22	861	19	798	23	661	23	1120	17	663	22	1130	18	1063	16	537	24	680	22	402	25	878	19	804	21	599	22	519	28	807	23	404	25	569	24	311	27	630	21	551	23	487	24	630	20	922	16	800	15	424	20	771	20	388	27	302	33	466	28	450	24	398	30	714	22	360	31	112	49	765	21	242	36	283	31	720	19	358	27	639	22	637	24	250	33	169	37	151	48	261	33	2264	11	1805	12	2321	11	336	27	446	26	364	29	627	21	832	21	452	26	393	33	293	31	0	.	78911	2
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	conifers	314	28	111	41	315	29	543	23	649	24	1122	23	4422	9	2846	11	1900	14	1712	14	154	45	96	39	263	33	22	.	271	41	674	26	1003	20	665	27	15	71	1006	19	1846	14	1281	15	1540	15	3478	10	3334	10	1735	15	525	25	1005	21	1512	17	186	41	497	23	342	34	211	34	869	21	247	30	366	27	646	21	549	25	391	32	910	21	348	34	1080	19	604	26	212	29	542	28	166	41	279	25	535	24	744	24	474	27	449	31	237	40	218	35	125	35	495	21	660	25	66	46	10	100	12	72	406	24	145	32	191	36	240	34	297	37	464	30	486	35	395	27	754	23	133	43	115	47	437	26	118	47	194	39	786	20	319	29	274	29	462	24	71	94	248	41	75	57	69	60	2176	11	1634	13	1921	13	312	33	232	44	315	29	432	24	613	24	151	34	191	43	272	33	0	.	61760	2
	broadleaves	841	18	335	28	280	31	594	23	109	39	378	38	806	20	795	18	1497	13	980	16	151	40	391	30	521	22	0	.	223	51	331	31	340	29	464	24	141	49	546	22	185	49	95	39	77	43	200	26	47	65	1960	12	532	29	209	25	305	31	194	32	121	36	140	40	104	45	185	27	126	37	70	62	229	30	268	27	355	35	261	28	369	27	641	22	648	21	325	30	374	26	299	28	612	20	277	30	95	56	254	34	262	28	271	33	392	26	14	60	115	26	104	53	202	27	386	20	486	23	315	25	306	24	286	23	104	38	38	94	28	61	224	42	71	66	204	29	199	31	73	69	291	29	273	35	72	46	53	44	135	27	499	27	188	40	257	37	119	44	45	52	142	39	29	42	10	75	228	28	223	36	248	31	210	26	287	26	347	24	232	34	231	38	150	32	0	.	27627	3
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	1155	16	446	25	594	22	1137	17	758	23	1500	20	5228	9	3640	10	3397	10	2692	11	304	32	488	27	784	21	22	.	494	33	1005	21	1342	18	1129	21	156	46	1552	16	2031	14	1376	14	1616	15	3679	10	3381	10	3695	10	1058	19	1214	19	1817	16	380	28	618	22	482	31	315	30	1053	19	374	26	437	27	876	19	817	22	746	25	1170	18	716	23	1721	17	1252	19	537	22	916	22	465	25	891	18	812	20	838	22	729	23	710	25	507	28	610	24	139	32	611	18	764	23	268	23	396	20	498	22	721	17	451	20	477	22	344	28	335	38	492	29	711	28	466	24	959	21	332	30	188	41	728	21	391	32	267	33	840	19	455	24	773	22	649	20	329	35	368	33	120	52	211	35	2204	11	1645	13	2149	12	535	26	480	29	525	24	718	21	960	20	382	27	422	31	423	26	0	.	89388	1
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	conifers	169	34	184	37	347	28	779	27	507	29	1282	23	4178	10	4014	11	2103	14	1303	17	104	80	233	48	206	40	40	.	139	44	487	30	973	21	640	24	0	.	783	21	1751	17	1216	16	1818	15	2765	11	2786	15	1371	14	495	26	978	20	1282	20	163	37	630	27	263	48	257	30	750	21	250	42	352	33	751	28	575	29	191	32	941	23	650	29	1294	19	370	26	307	42	259	33	140	67	292	36	658	25	711	21	456	29	392	40	168	37	223	32	258	29	710	21	393	28	43	50	12	100	38	58	464	21	164	39	123	38	238	36	136	41	260	33	411	32	363	31	996	22	200	46	61	54	456	30	136	63	241	34	392	23	424	32	378	28	513	32	91	65	165	60	198	56	208	43	2325	12	1448	15	1657	14	485	29	449	32	258	31	480	28	415	27	254	53	239	39	184	39	0	.	60240	2
	broadleaves	580	24	390	27	193	42	340	31	58	74	244	36	949	26	510	20	1319	17	610	21	240	36	156	44	537	27	0	.	203	48	276	33	303	30	379	31	65	71	305	29	131	40	127	42	27	59	164	38	28	74	917	15	546	35	185	39	210	38	149	43	16	86	138	46	0	.	15	100	120	46	20	100	322	39	303	38	106	46	194	36	302	27	337	25	383	28	505	25	129	45	352	37	461	24	182	37	94	84	148	36	283	40	230	44	302	29	0	0	77	42	26	79	138	32	261	27	158	38	212	28	152	45	306	28	73	64	12	100	0	.	109	44	150	73	122	43	198	34	0	.	166	39	216	45	0	.	34	55	122	42	377	36	237	41	102	51	80	45	92	50	137	40	35	69	22	100	123	41	109	41	208	39	196	35	280	37	325	26	219	38	172	42	354	31	0	.	20679	4
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0
	total	748	21	573	25	540	24	1118	21	565	28	1526	22	5126	10	4524	11	3422	11	1914	14	343	35	388	36	743	23	40	.	343	34	763	23	1276	18	1019	20	65	71	1088	18	1881	16	1343	16	1845	14	2929	11	2815	15	2288	11	1041	24	1163	20	1491	19	312	29	646	27	400	37	257	30	765	20	370	32	371	32	1073	23	878	24	297	28	1135	21	952	23	1631	17	753	22	812	24	388	31	492	35	753	22	841	22	805	21	604	26	675	29	398	37	525	24	258	29	787	20	419	26	180	28	273	26	196	34	676	17	316	29	429	22	311	32	148	38	260	33	520	28	513	32	1118	22	398	31	61	54	623	25	352	38	241	34	426	22	546	28	756	24	750	26	192	41	246	43	290	43	345	31	2359	12	1470	15	1780	13	594	27	658	29	454	24	761	26	740	21	472	36	410	33	539	27	0	.	80919	2
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	conifers	163	41	124	63	128	54	567	26	211	41	775	29	2195	14	2437	14	1708	16	654	29	17	100	117	54	52	100	0	.	99	54	258	38	536	32	349	31	0	.	428	27	890	20	621	22	941	18	1701	13	1278	20	790	20	704	30	718	24	727	24	110	47	229	30	75	71	155	52	885	21	118	43	260	35	298	34	222	32	205	36	505	27	288	31	1103	24	240	38	285	34	71	51	68	51	86	71	601	27	239	34	278	35	182	48	49	71	156	58	171	46	275	28	337	30	25	71	107	70	30	100	145	33	71	51	172	56	94	52	130	41	149	45	269	34	244	35	460	34	66	54	58	100	417	38	88	62	122	58	247	29	382	33	180	34	144	39	20	100	192	42	21	100	180	53	1312	17	688	18	902	20	134	49	270	46	142	47	334	35	170	40	87	63	61	77	308	44	0	.	35101	3
	broadleaves	409	26	253	34	110	45	235	36	29	71	152	42	355	35	236	37	458	23	74	51	264	40	99	45	294	36	0	.	79	58	85	52	122	49	193	45	20	100	122	45	45	71	47	59	12	100	69	57	9	100	288	28	246	42	159	47	78	62	54	100	0	.	0	.	0	.	0	.	36	71	0	.	156	55	143	43	26	75	108	82	97	54	150	43	284	42	248	42	21	100	155	49	142	53	105	47	0	.	121	60	30	71	103	55	116	47	19	100	53	54	7	100	83	58	76	41	152	39	145	37	24	71	80	45	32	73	0	.	0	.	66	63	64	58	0	.	88	50	0	.	33	71	117	50	0	.	0	.	0	.	45	73	45	73	29	71	101	52	14	100	201	54	0	.	0	.	71	46	124	56	207	38	248	39	188	40	138	47	59	58	42	71	151	52	0	.	9334	6
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	573	24	376	36	238	36	802	21	239	39	927	27	2549	13	2673	13	2165	14	728	27	281	38	217	42	346	34	0	.	178	39	343	32	658	28	542	28	20	100	550	23	935	19	667	21	954	18	1770	12	1287	20	1077	17	951	25	878	23	805	23	165	46	229	30	75	71	155	52	885	21	154	37	260	35	454	29	365	28	231	34	613	26	386	30	1253	22	524	31	534	28	92	45	224	37	228	42	706	25	239	34	399	30	213	43	151	43	272	40	190	43	328	25	344	30	108	47	183	44	182	40	290	25	96	42	251	40	126	43	130	41	149	45	334	31	309	32	460	34	154	37	58	100	450	36	205	42	122	58	247	29	382	33	225	35	188	36	49	58	293	33	35	72	381	45	1312	17	688	18	974	19	258	39	477	33	390	33	522	27	308	33	146	47	103	54	459	35	0	.	44435	3
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	conifers	24	100	65	58	80	79	224	60	122	60	232	41	1206	19	1230	17	759	22	434	35	0	.	46	71	20	100	25	.	53	100	135	51	142	52	252	39	19	100	489	29	608	28	449	27	656	23	1117	19	465	22	427	29	275	40	210	45	312	37	147	62	126	53	50	71	174	43	203	45	15	100	156	43	219	39	169	58	125	48	216	50	417	36	677	29	51	71	217	43	0	.	0	.	0	.	366	36	144	45	267	51	51	71	98	74	80	58	227	33	222	41	163	38	0	.	0	.	0	.	116	42	117	53	68	60	100	46	175	68	236	54	98	48	255	37	297	36	61	71	0	.	101	50	25	100	30	100	51	72	153	53	67	58	101	52	0	.	45	71	86	76	75	100	1270	20	268	34	579	32	154	43	54	71	101	50	79	59	99	60	26	100	51	71	110	50	0	.	19706	4
	broadleaves	221	41	150	54	62	71	245	39	0	.	28	100	90	63	136	61	356	40	46	71	62	71	49	71	157	54	0	.	27	100	67	64	38	71	127	63	0	.	0	.	56	75	39	85	12	100	11	100	0	.	139	42	60	71	0	.	85	64	42	71	0	.	0	.	23	100	0	.	0	.	0	.	0	.	89	58	49	75	66	59	100	61	50	72	68	58	82	61	0	.	55	73	74	78	0	.	0	.	108	74	49	74	61	71	26	100	0	.	31	58	5	100	57	78	86	42	44	51	119	42	57	80	81	42	16	100	0	.	20	100	56	71	38	71	0	.	122	66	0	.	0	.	164	56	0	.	0	.	0	.	29	100	0	.	0	.	72	61	0	.	42	71	0	.	0	.	45	61	0	.	32	100	113	50	49	71	0	.	58	71	26	100	27	100	0	.	4597	9
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	245	38	215	41	142	54	469	35	122	60	260	38	1297	19	1366	16	1115	20	481	33	62	71	96	50	177	49	25	.	80	74	202	40	180	44	379	34	19	100	489	29	664	27	489	26	668	22	1127	19	465	22	567	24	334	35	210	45	397	32	189	51	126	53	50	71	198	40	203	45	15	100	156	43	219	39	258	43	174	40	282	41	517	32	727	28	119	45	299	35	0	.	55	73	74	78	366	36	144	45	375	48	100	51	159	53	106	61	227	33	252	36	168	37	57	78	86	42	44	51	235	29	173	44	148	35	115	42	175	68	256	51	154	40	293	33	297	36	184	60	0	.	101	50	189	51	30	100	51	72	153	53	96	50	101	52	0	.	117	56	86	76	117	69	1270	20	268	34	624	30	154	43	86	58	215	36	129	46	99	60	84	58	76	58	137	45	0	.	24303	4
81-90 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	conifers	0	.	0	.	89	58	165	52	0	.	51	100	420	31	555	27	367	34	165	51	32	100	33	100	0	.	0	.	32	100	43	100	81	58	40	100	0	.	148	43	193	49	292	33	282	32	509	25	267	32	143	45	0	.	0	.	58	72	31	100	35	100	0	.	0	.	110	76	22	100	42	100	49	100	0	.	54	100	69	58	67	71	351	55	34	100	67	71	29	100	0	.	0	.	83	71	31	100	85	59	0	.	29	100	117	71	37	100	174	44	144	47	34	100	0	.	0	.	85	69	31	100	23	100	0	.	49	100	0	.	113	54	175	47	105	51	90	59	0	.	44	100	0	.	0	.	38	100	0	.	0	.	39	100	0	.	33	100	44	100	26	100	417	28	195	36	169	41	0	.	0	.	30	100	131	50	34	100	31	100	60	100	0	.	0	.	7619	7
	broadleaves	111	63	32	100	25	100	0	.	0	.	0	.	82	72	73	74	102	59	0	.	0	0	0	.	41	100	0	.	0	.	0	.	23	100	33	100	0	.	14	100	0	.	0	.	0	.	24	72	0	.	63	59	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12	100	0	.	0	.	71	73	27	100	0	.	39	100	0	.	27	100	103	58	0	.	27	100	0	.	0	.	0	.	82	71	34	100	41	100	37	100	0	.	65	61	9	100	14	100	65	62	24	72	14	100	0	.	39	73	0	.	0	.	25	100	0	.	0	.	0	.	0	.	0	.	0	.	68	62	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	38	71	0	.	159	51	86	100	0	.	48	100	28	100	85	59	0	.	0	.	1890	14
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	111	63	32	100	113	51	165	52	0	.	51	100	502	28	628	25	469	30	165	51	32	100	33	100	41	100	0	.	32	100	43	100	104	50	73	71	0	.	162	40	193	49	292	33	282	32	533	24	267	32	206	36	0	.	0	.	58	72	31	100	35	100	0	.	0	.	110	76	34	100	42	100	49	100	71	73	81	75	69	58	106	58	351	55	61	71	170	45	29	100	27	100	0	.	83	71	31	100	167	46	34	100	70	100	154	59	37	100	239	36	152	44	48	77	65	62	24	72	99	61	31	100	61	59	0	.	49	100	25	100	113	54	175	47	105	51	90	59	0	.	44	100	68	62	0	.	38	100	0	.	0	.	39	100	0	.	33	100	44	100	26	100	417	28	195	36	207	36	0	.	159	51	116	78	131	50	82	72	58	71	145	54	0	.	0	.	9509	6
91-100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	conifers	0	.	0	.	0	.	0	.	0	.	0	.	199	50	110	72	86	71	36	100	0	.	0	.	0	.	0	.	44	100	0	.	27	100	0	.	0	.	133	45	50	100	56	73	76	71	110	59	199	44	72	71	31	100	0	.	0	.	0	.	46	100	39	100	0	.	88	100	0	.	0	.	119	79	0	.	33	100	24	100	0	0	101	71	0	.	0	.	0	.	0	.	0	.	35	100	0	.	0	.	0	.	0	.	0	.	20	100	24	100	38	100	0	.	0	.	0	.	87	71	0	0	0	.	0	.	25	100	37	100	0	.	69	100	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	34	100	0	.	0	.	166	43	108	51	0	.	0	.	0	.	28	100	0	.	0	.	43	100	0	.	0	.	0	.	2390	13
	broadleaves	26	100	0	.	0	.	33	100	0	.	0	.	19	100	0	.	79	100	0	.	0	.	45	100	0	.	0	.	0	.	0	.	27	100	0	.	0	.	0	.	0	.	0	.	0	.	37	76	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	37	100	0	.	0	.	24	100	0	.	95	100	0	.	0	.	56	71	0	.	0	.	0	0	27	72	0	.	0	.	11	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	41	100	0	.	0	.	0	.	0	.	0	.	0	.	34	100	0	.	0	.	0	.	0	.	46	100	0	.	0	.	50	100	0	.	0	.	0	.	0	.	0	.	0	.	688	26
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	26	100	0	.	0	.	33	100	0	.	0	.	218	47	110	72	165	61	36	100	0	.	45	100	0	.	0	.	44	100	0	.	54	71	0	.	0	.	133	45	50	100	56	73	76	71	148	48	199	44	72	71	31	100	0	.	0	.	0	.	46	100	39	100	0	.	88	100	0	.	0	.	119	79	0	.	33	100	24	100	0	0	101	71	0	.	0	.	0	.	0	.	37	100	35	100	0	.	24	100	0	.	95	100	0	.	20	100	80	58	38	100	0	.	0	0	27	72	87	71	0	0	11	100	0	.	25	100	37	100	0	.	69	100	0	.	0	0	0	.	0	.	41	100	0	.	0	.	0	.	0	.	0	.	0	.	68	71	0	.	0	.	166	43	108	51	46	100	0	.	0	.	78	73	0	.	0	.	43	100	0	.	0	.	0	.	3078	12
>100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	conifers	0	.	41	100	0	.	47	100	0	.	0	.	84	78	115	73	40	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	38	100	0	.	137	61	40	100	290	78	115	58	255	44	69	58	39	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	43	100	0	.	0	.	22	100	0	.	35	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	58	71	0	.	0	.	0	.	116	79	190	51	0	.	0	.	0	.	0	.	70	71	0	.	65	71	0	.	0	.	43	100	0	.	21	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	46	71	0	.	62	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2079	17
	broadleaves	0	.	35	100	0	.	0	.	0	.	0	.	54	100	0	.	0	.	0	.	0	.	60	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	21	100	0	.	0	.	0	.	18	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	126	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	242	100	0	.	42	71	0	.	214	100	192	100	61	75	265	58	0	.	100	59	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	39	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	32	100	0	.	1500	30
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	75	71	0	.	47	100	0	.	0	.	137	62	115	73	40	100	0	.	0	.	60	100	0	.	0	.	0	.	0	.	0	.	38	100	0	.	158	54	40	100	290	78	115	58	273	42	69	58	39	100	0	.	0	.	0	.	0	.	0	.	0	.	126	100	0	.	0	.	0	.	43	100	0	.	0	.	22	100	0	.	35	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	58	71	0	.	0	.	242	100	116	79	231	44	0	.	214	100	192	100	61	75	336	48	0	.	164	45	0	.	0	.	43	100	0	.	21	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	39	100	0	.	0	.	0	.	0	.	0	.	0	.	46	71	0	.	62	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	32	100	0	.	3579	16
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	conifers	1121	22	1146	27	1737	23	2791	19	2391	23	4590	19	18890	8	14980	9	10083	10	6610	12	419	45	687	37	838	27	101	.	1030	31	2938	20	4450	16	2983	20	81	65	4354	16	8128	12	6249	13	8259	11	15411	8	14111	9	7208	11	3093	19	4368	16	5693	15	893	30	2316	19	1129	30	1064	27	3850	18	1299	25	2259	21	3277	17	2437	21	1687	25	4024	16	2226	22	5586	16	2160	18	1553	23	1249	24	567	34	1080	24	3199	18	2931	19	2183	23	1845	26	1121	28	1604	23	1396	22	3124	16	2521	19	295	32	161	69	151	58	2175	15	817	24	1085	28	1239	26	1304	29	2016	23	1871	25	1999	22	3662	19	944	29	377	45	2180	22	609	37	846	28	2672	16	1695	24	1393	21	1728	22	408	53	964	32	575	46	789	33	12246	8	8026	11	9123	10	1613	23	1505	27	1263	25	2125	20	2196	21	964	32	959	30	1309	26	0	.	286604	1
	broadleaves	3421	15	1806	19	1265	21	2627	19	440	31	1451	26	4620	15	3667	12	6257	10	4295	11	968	29	1420	23	2603	19	0	.	921	36	1326	26	1834	20	2341	21	458	41	1934	17	836	27	821	23	710	22	1847	16	494	32	6594	10	2123	23	1200	23	1486	22	664	27	477	29	519	34	467	34	537	23	924	27	270	49	1613	24	1497	22	1210	27	1392	20	1831	21	2245	18	2717	15	1818	20	1392	20	1464	22	2448	18	1337	21	454	36	1212	27	1447	23	1254	27	1760	23	291	32	1057	21	495	30	2064	19	2639	17	3286	13	2740	15	1979	18	2475	16	677	33	170	48	217	43	1013	28	730	30	937	23	1080	27	203	40	1388	23	1128	30	408	36	366	27	785	23	1606	22	1127	25	750	30	556	38	221	43	774	35	587	21	592	21	2210	14	704	28	1225	26	1223	27	1492	20	1461	19	1112	27	907	29	1055	24	0	.	134450	2
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	0	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0
	total	4543	14	2952	17	3002	17	5418	14	2832	22	6041	17	23510	7	18647	8	16340	8	10906	10	1387	26	2107	22	3441	18	101	.	1951	25	4265	17	6285	14	5324	16	539	39	6288	14	8964	11	7070	12	8969	10	17257	7	14606	8	13802	8	5216	16	5567	15	7180	14	1558	23	2793	18	1648	26	1531	25	4387	17	2223	21	2530	20	4890	16	3934	18	2897	19	5416	15	4057	18	7831	14	4876	14	3370	17	2641	18	2031	20	3528	16	4536	16	3385	18	3395	19	3292	20	2375	23	3364	18	1687	20	4181	14	3016	17	2359	17	2799	17	3437	12	4916	11	2797	16	3560	15	1916	23	1474	27	2233	22	2885	20	2729	19	4599	17	2024	24	580	38	3569	19	1738	27	1254	25	3039	16	2480	20	3000	17	2855	18	1159	29	1520	28	796	42	1563	27	12832	8	8618	10	11333	9	2317	21	2730	21	2487	20	3617	17	3657	17	2077	23	1866	24	2363	21	0	.	421054	1

Table citation

Abegg, M.; Ahles, P.; Allgaier Leuch, B.; Cioldi, F.; Didion, M.; Düggelin, C.; Fischer, C.; Herold, A.; Meile, R.; Rohner, B.; Rösler, E.; Speich, S.; Temperli, C.; Traub, B.,
2023: Swiss national forest inventory - Result table No. 2298043. Birmensdorf, Swiss Federal Research Institute WSL

back